Update app1.py

app1.py

@@ -1,68 +1,60 @@
-from typing import TypedDict, Annotated
-from langgraph.graph.message import add_messages
-from langchain_core.messages import AnyMessage, HumanMessage, AIMessage
-from langgraph.prebuilt import ToolNode
-from langgraph.graph import START, StateGraph
-from langgraph.prebuilt import tools_condition
-from langgraph.checkpoint.memory import InMemorySaver  
-from langchain_ollama import ChatOllama
-from tool import DuckDuckGoSearchRun,web_search_tool,latest_news_tool, get_weather_tool as weather_info_tool, hub_stats_tool
-from retriever import guest_info_tool_1
-
-
-
-
-model = ChatOllama(
-        model="qwen2.5:1.5b",  # Or try other Ollama-supported models
-        base_url="http://127.0.0.1:11434",  # Default Ollama local server
-            num_predict=256
-        )
-
-tools=[weather_info_tool,web_search_tool,weather_info_tool,hub_stats_tool,guest_info_tool_1]
-model_with_tool = model.bind_tools(tools)
-
-class AgentState(TypedDict):
-    messages: Annotated[list[AnyMessage],add_messages]
-
-
-def assistant(state: AgentState):
-    return {
-        "messages": [model_with_tool.invoke(state["messages"])],
-    }
-
-builder = StateGraph(AgentState)
-
-builder.add_node("assistant",assistant)
-builder.add_node('tools',ToolNode(tools))
-
-builder.add_edge(START,'assistant')
-builder.add_conditional_edges(
-    'assistant',
-    tools_condition
-)
-builder.add_edge('tools','assistant')
-
-checkpointer = InMemorySaver()
-
-alfred = builder.compile(checkpointer=checkpointer)
-thread_config = {"configurable": {"thread_id": "1"}}
-
-print("🎩 Alfred: Hello, I am Alfred. How can I assist you today?")
-print("Type 'exit' or 'quit' to stop.\n")
-
-while True:
-    user_input = input("You: ")
-
-    if user_input.lower() in ["exit", "quit"]:
-        print("Alfred: Goodbye.")
-        break
-
-    response = alfred.invoke(
-        {"messages": [HumanMessage(content=user_input)]},
-        thread_config
-    )
-
-    ai_reply = response["messages"][-1].content
-
-    print("\n🎩 Alfred:", ai_reply)
-    print("-" * 40)
+from typing import TypedDict, Annotated
+from langgraph.graph.message import add_messages
+from langchain_core.messages import AnyMessage, HumanMessage, AIMessage
+from langgraph.prebuilt import ToolNode
+from langgraph.graph import START, StateGraph
+from langgraph.prebuilt import tools_condition
+from langgraph.checkpoint.memory import InMemorySaver  
+from tool import DuckDuckGoSearchRun,web_search_tool,latest_news_tool, get_weather_tool as weather_info_tool, hub_stats_tool
+
+from langchain_huggingface import HuggingFaceEndpoint
+
+
+
+model = HuggingFaceEndpoint(
+    repo_id="Qwen/Qwen2.5-7B-Instruct",
+    task="text-generation",
+    max_new_tokens=256
+)
+
+tools=[weather_info_tool,web_search_tool,weather_info_tool,hub_stats_tool]
+model_with_tool = model.bind_tools(tools)
+
+class AgentState(TypedDict):
+    messages: Annotated[list[AnyMessage],add_messages]
+
+
+def assistant(state: AgentState):
+    return {
+        "messages": [model_with_tool.invoke(state["messages"])],
+    }
+
+builder = StateGraph(AgentState)
+
+builder.add_node("assistant",assistant)
+builder.add_node('tools',ToolNode(tools))
+
+builder.add_edge(START,'assistant')
+builder.add_conditional_edges(
+    'assistant',
+    tools_condition
+)
+builder.add_edge('tools','assistant')
+
+checkpointer = InMemorySaver()
+
+alfred = builder.compile(checkpointer=checkpointer)
+thread_config = {"configurable": {"thread_id": "1"}}
+
+
+
+def run_agent(question: str):
+
+    response = alfred.invoke(
+        {"messages":[HumanMessage(content=question)]},
+        {"configurable":{"thread_id":"evaluation"}}
+    )
+
+    answer = response["messages"][-1].content
+
+    return answer